System and method for bayesian matching of web search results

ABSTRACT

Provided are a system and method for matching search results from multiple websites. In one example, the method includes calculating a probability that a search result of a first website corresponds to a same item as a search result of a second website based on Bayes theorem, in response to the calculated probability being greater than a predetermined threshold, determining that the search result of the first website and the search result of the second website are a match, and displaying an aggregated list of search results combined from the first website and the second website based on the matched search results. By auto-matching search results using Bayes theorem, a true match can be determined that is more accurate in comparison to a manual matching operation performed by a human.

BACKGROUND

Various search engines and comparison websites compare web contentassociated with an item, often for purchase, from multiple sources andprovide a requesting user with a comparison of attributes of the itemfrom the web content, for example, a price comparison, featurecomparison, availability comparison, and other features. One industrywhere such comparisons often take place is in the retail industry whereweb visitors can filter and compare attributes of items such asproducts, services, hotel accommodations, flights, and the like. Toprovide the viewer with a comparison of the same item from multiplewebsites, a comparison site typically collects web content from multiplewebsites and stores the collected web content in a large centralizeddatabase. An engineer of the database (e.g., a manager, operator,technician, etc.) then attempts to manually match web content associatedwith the item together from multiple websites. For example, the engineermay compare search results on a first website to search results on asecond website to determine if the two search results correspond to thesame item (e.g., product, service, hotel listing, flight accommodations,or the like). When each site has a respective search resultcorresponding to the same item, the search results are determined to bea match, and the web content associated therewith may be compared witheach other or one of the search results may be removed to provide aconsolidated lists of search results from the combined search results ofboth sites.

However, one of the drawbacks of manually determining that searchresults are associated with the same item is that human error can causemistakes in the matching process or fail to identify matches. Forexample, a human may fail to identify or incorrectly identify that ahotel listing on a first website corresponds to a hotel listing on asecond website, because of a difference between one or more attributessuch as the hotel name, address, geo-location, and the like, between thesearch results/listings on the two sites. Another drawback is the amountof time that it takes the engineer to manually view web contentassociated with search results from across multiple websites anddetermine which search results are for die same item. As a non-limitingexample, for a single hotel comparison on a travel related website, thewebsite may collect a price for the hotel from twenty different hotelrelated websites in order provide one comprehensive price comparisonsearch result of hotel. To gather web content associated with the hotelfrom those twenty sites, the engineer must first match twenty searchresults from these twenty sites through a manual process.

Accordingly, what is needed is an automated system for matching webcontent from multiple websites and databases, which does not require amanual matching process and which is immune from or has a reducedpossibility of human error.

SUMMARY

According to an aspect of an example embodiment, provided is a methodfor matching search results from multiple websites, the method includingcalculating a probability that a search result of a first websitecorresponds to a same item as a search result of a second website basedon Bayes theorem, in response to the calculated probability beinggreater than a predetermined threshold, determining that the searchresult of the first website and the search result of the second websiteare a match, and displaying an aggregated list of search resultscombined from the first website and the second website based on thematched search results.

According to an aspect of another example embodiment, provided is acomputing device for matching search results from multiple websites, thecomputing device including a processor configured to calculate aprobability that a search result of a first website corresponds to asame item as a search result of a second website based on Bayes theorem,and, in response to the calculated probability being greater than apredetermined threshold, determine that the search result of the firstwebsite and the search result of the second website are a match, and anoutput configured to output, to a display device, an aggregated list ofsearch results combined from the first website and the second websitebased on the matched search results.

According to an aspect of another example embodiment, provided is anon-transitory computer readable medium having stored thereininstructions that when executed cause a computer to perform a method formatching search results from multiple websites, the method includingcalculating a probability that a search result of a first websitecorresponds to a same item as a search result of a second website basedon Bayes theorem, in response to the calculated probability beinggreater than a predetermined threshold, determining that the searchresult of the first website and the search result of the second websiteare a match, and displaying an aggregated list of search resultscombined from the first website and the second website based on thematched search results.

Other features and aspects may be apparent from the following detaileddescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

Features and advantages of the example embodiments, and the manner inwhich the same are accomplished, will become more readily apparent withreference to the following detailed description taken in conjunctionwith the accompanying drawings.

FIG. 1 is a diagram illustrating a system for Bayesian matching ofsearch results from multiple sources in accordance with an exampleembodiment.

FIG. 2 is a diagram illustrating a process of matching search resultsfrom multiple websites in accordance with an example embodiment.

FIG. 3 is a diagram illustrating a method for matching search results inaccordance with an example embodiment.

FIG. 4 is a diagram illustrating a computing device for matching searchresults in accordance with an example embodiment.

Throughout the drawings and the detailed description, unless otherwisedescribed, the same drawing reference numerals will be understood torefer to the same elements, features, and structures. The relative sizeand depiction of these elements may be exaggerated or adjusted forclarity, illustration, and/or convenience.

DETAILED DESCRIPTION

In the following description, specific details are set forth in order toprovide a thorough understanding of the various example embodiments. Itshould be appreciated that various modifications to the embodiments willbe readily apparent to those skilled in the art, and the genericprinciples defined herein may be applied to other embodiments andapplications without departing from the spirit and scope of thedisclosure. Moreover, in the following description, numerous details areset forth for the purpose of explanation. However, one of ordinary skillin the art should understand that embodiments may be practiced withoutthe use of these specific details. In other instances, well-knownstructures and processes are not shown or described in order not toobscure the description with unnecessary detail. Thus, the presentdisclosure is not intended to be limited to the embodiments shown, butis to be accorded the widest scope consistent with the principles andfeatures disclosed herein.

According to various embodiments, provided herein is a system and methodfor matching web content from across multiple websites using Bayestheorem. In particular, a search result from a first website may bematched to a search result from a second website (i.e., identified asbeing associated with a same item) using Bayes theorem. For example, thematching may be performed by a host computer of a search engine site ora comparison site that may provide an aggregated list of search resultsfrom multiple websites or provide a comparison of content (e.g., price)from multiple websites within a unified search result. For example, asearch result from a first website may be compared with a search resultfrom a second website to determine whether the two search resultscorrespond to the same item, thing, product, hotel listing, service, andthe like. When the two search results are determined to be a match tothe same item, the host may perform deduplication of the matched searchresults such that only one search result is provided within anaggregated list of search results or the host may generate a comparisonof the web content from the matched search results. The host may alsodisplay search results based on the matching.

Related comparison sites and search engines typically require a human toperform matching of search results from different websites. In otherwords, a human must take the time to visually compare a first searchresult on a first website to a second search result on a second websiteand make a determination (or guess) as to whether the two search resultsare for the same item. As a result, there is often human error in thematching process or a failure by the human to detect two search resultsas being the same given the massive amount of data involved. Somerelated sites are capable of automatically detecting that a searchresult of a first website is a duplicate of a search result of a secondwebsite if the web content included in the two search results is aperfect match with the one another. However, if there is a differencebetween any of the name of an item, address, description, etc., of twosearch results (even though the search results refer to the same item,product, hotel, etc.), the automated matching process is not able tomatch the two search results. As a result, automated matching in arelated art does not provide any room for differences (or mistakes)between search results no matter how minor.

According to various aspects, matching between two search results of webcontent may be performed using Bayesian matching. In testing performedby the inventors, a training set of correctly matched hotel rentalcomparisons (e.g., 10,000 matched hotel listings) was built. The matchedset was analyzed and determined to have a range of distribution in theaccuracy level of matching hotel search results. In other words, not allmatching hotel listings were a perfect match with each other. Bayestheorem was applied to the search results of the hotel listings toidentify a statistically optimal solution of what hotel listings are amatch even in cases where the listings were not an identical match.Using a plurality of dimensions or factors such as geolocation, hotelnames, words/numbers in the address, amenities, star ratings, and thelike, the example embodiments identify the Bayesian prior and combinethe probabilities in a mathematically optimal way to get the bestpossible answer. The Bayesian matching is performed by acomputer/processor and has shown to be more accurate than human-poweredclassification. That is, when human matched search results on test datastrayed from the Bayesian matched search results during testing of thesame data, it was because the human matched results were incorrect.

FIG. 1 illustrates a system 100 for Bayesian matching of search resultsfrom multiple sources in accordance with an example embodiment.Referring to FIG. 1, the system 100 includes a user device 110, aBayesian matching server 120, and a plurality of content server 130,134, and 138. The components of the system 100 may be connected to eachother through a network such as the Internet, a private network, or acombination thereof. Also, the network may be a wired network, awireless network, or a combination thereof. The user device 110 may be,for example, a computer, a laptop, a notepad, a tablet, a mobile device,a smart wearable device, an appliance, a kiosk, a television, and thelike. The Bayesian matching server 120 may be a web server that hostsone or more websites and/or that is connected to one or more hostservers hosting websites. The plurality of content servers 130, 134, and138 may each host one or more respective websites and may have contentstored therein associated with the websites.

In an example operation, the user device 110 may connect to the Bayesianmatching server 120 when a user inputs an address of a website hosted bythe Bayesian matching server 120 into a web browser executing on theuser device. For example, the web browser may be Microsoft InternetExplorer, Apple Safari, Google Chrome, and the like. As another example,the web browser may be a mobile browser in a case in which the userdevice 110 corresponds to a mobile device, tablet, smart wearabledevice, etc. The user device 110 may request the Bayesian matchingserver 120 for a search request (e.g., keyword search) corresponding toa product, service, hotel listing, travel accommodation, and the like.In response, the Bayesian matching server 120 may perform a searchoperation across the Internet and gather search results from a pluralityof websites hosted by the plurality of content servers 130, 134, and138, based on the search request. The Bayesian matching server 120 maycollect search results from respective websites hosted by the pluralityof content servers 130, 134, and 138 based on the search request.

In the example of FIG. 1, the Bayesian matching server 120 may host awebsite such as a search engine, a comparison site, a content providingsite, and the like. The Bayesian matching server 120 may be connected tothe plurality of content servers 130, 134, and 138, and collect webcontent from across the content servers 130, 134, and 138 (e.g., fromwebsites hosted by the content servers 130, 134, and 138). For example,the Bayesian matching server 120 may collect retail content, travelrelated content, news related content, entertainment content, and thelike, from across the multiple content server 130, 134, and 138. Forconvenience of explanation, some examples herein refer to travel relatedweb content such as vacation rentals, vacation home rentals, hotelaccommodations, and the like, however, it should be appreciated thatother types of web content may be used such as retail web content, newscontent, medical content, entertainment content, and the like, withoutany difference in the function of the system and methods.

According to various embodiments, the Bayesian matching server 120 mayprovide search results from the plurality of content servers 130, 134,and 138 to the user device 110 based on matched search results inresponse to a search request input by a user. For example, the Bayesianmatching server 120 may provide an aggregated list of search results ora comparison of search results from the plurality of websites to theuser device 110. In order to generate an aggregated list of searchresults that is potentially easier and more efficient for a user to viewand navigate through, the Bayesian matching server 120 may match twosearch results from two respective websites that correspond to a sameunique item (e.g., product, service, hotel accommodation) and removeredundant search results. That is, the Bayesian matching server 120 mayperform deduplication such that only a single instance of a searchresult for that particular item is included in the aggregated list ofsearch results instead of multiple instances. As another example, theBayesian matching server 120 may match search results corresponding tothe unique item from multiple websites, extract content from the searchresults, and provide a single unified search result for the item withcontent included from multiple sites and multiple search results such asa plurality of prices, availability, different features, and the like.

Rather than perform a manual matching operation, the Bayesian matchingserver 120 according to example embodiments can automatically matchsearch results (e.g., auto-match) based on Bayes theorem. For example,one or more features from web content of a first search result and oneor more features from web content of a second search result may becompared using Bayes theorem to determine if the two search results, andthe respective web content, correspond to the same unique search result(i.e., item). In an example of comparing two rental property listings asthe two search results, one or more of a name, an address, ageolocation, a rating, amenities, and the like, of the two respectiverental properties may be compared with each other using Bayes theorem todetermine if the two rental property listings are for the same piece ofproperty. In this case, the Bayes theorem may be used to calculate aprobability that the two rental property listings (e.g., hotel, vacationrental, etc.) correspond to the same property based on corresponding webcontent of the rental property listings. According to variousembodiments, if the probability that the two search results correspondto the same unique item is above a predetermined threshold, the twosearch results may be determined as being directed towards the sameitem. Furthermore, the Bayesian matching server 120 may generate a listof search results based on the matching, for example, by removingredundant search results, comparing search results for a same item, andthe like, based on the determined matching search results. In theexample of FIG. 1, the Bayesian matching server 120 may provide thesearch results to the user device 110 such as through a web browserexecuting on the user device 110.

FIG. 2 illustrates a process of matching search results from multiplewebsites in accordance with an example embodiment. For example, theprocess illustrated in the example of FIG. 2 may be performed by theBayesian matching server 120 shown in FIG. 1 using search resultscollected from contents servers such as content servers 130, 134, and138. In this example, a plurality of search results 202, 204, 206, and208 from a first website are received and a plurality of search results212, 214, 216, and 218 are received from a second website. In thisnon-limiting example, the search results from the first website includea first list of rental properties and the search results from the secondwebsite include a second list of rental properties. For example, thesearch results 202-208 and 212-218 from both the first website and thesecond website may be the results from a search request for a hotel orrental listing in a particular area (e.g., city, town, neighborhood, zipcode, state, and the like) input into a search bar of a website hostedby the Bayesian matching server 220 or hosted by another computingdevice. The Bayesian matching server 220 may then perform a search onthe first and second websites using the same search request, and aplurality of search results 202-208 and 212-218 may be provided.

According to various embodiments, a search result of a first website anda search result of a second website may correspond to the same uniqueproduct, item, hotel listing, or the like, however, the web contentassociated with the two search results may not be an exact match witheach other for one or more reasons. For example, one or more of a name,description, features, amenities, geolocation, address, and the like,may be different between two search results for two hotel listings orvacation property rental listings on two different websites. In theexample of FIG. 2, search result 202 of the first website corresponds tothe same unique rental property listing as search result 216 of thesecond website, however, both the name of the rental property and therating of the rental property are different on the respective site, andtherefore not a perfect match with one another. Therefore, according tovarious embodiments, web content from the search result 202 may be inputinto Bayes theorem along with web content from the search result 216, todetermine the probability that the two search results 202 and 216correspond to the same rental property.

Many websites perform comparison of items, products, travelaccommodations, and the like. For example, a user can search websitesfor finding the cheapest price on books, cars, hotels, consumerelectronics, services, and the like. However, the matching process formatching search results together is typically performed manually by ahuman or a matching process that requires a perfect match. In theexample of FIG. 2, it is not obvious from viewing search result 202 andsearch result 216 that the two search results correspond to the sameunique hotel listing because the hotels are named differently, havedifferent review ratings, and have different representative images onthe respective websites. As a result, it is difficult for a user tomanually detect that the two search results 202 and 216 correspond tothe same rental property listing. Furthermore, there are over onemillion hotels available globally. Everyday there are hundreds of hotelsopening and hundreds of hotels closing all around the world. As aresult, websites are always adding and removing hotels from the pool ofreturnable search results. Therefore, a website may provide searchresults for over a million hotel accommodations. Sifting through thedata, manually, can be an exhaustive process.

To eliminate these problems, the example embodiments use Bayes theoremin an automated process to determine a probability (e.g., a likelihood)that the two search results 202 and 216 are to the same unique rentalproperty listing. By using Bayes theorem instead of requiring a perfectmatch, the example embodiments can provide wiggle room between webcontent of two search results while still auto-matching two searchresults corresponding to the same item. For example, one or more of ageolocation, a description, keywords and/or numbers from an address,keywords from a name, and the like, of the two respective rentalproperties may be input into Bayes theorem, and results thereof may becombined, to determine if the two rental property listings have aprobability of corresponding to the same rental property. In thisexample, the probability that the two search results 202 and 216 are forthe same unique hotel listing is determined to be above a predeterminedthreshold even though various web content features are not an identicalmatch. Accordingly, the Bayesian matching server 220 determines thatsearch result 202 and search result 216 match the same rental property.

FIG. 3 illustrates a method 300 for matching search results frommultiple websites in accordance with an example embodiment. For example,the method 300 may be performed by the Bayesian matching server 120 or220 shown in FIG. 1 or 2. Referring to FIG. 3, in 310, the methodincludes collecting search results from a plurality of sources includingat least a first website and a second website. For example, the searchresults may be received from host servers hosting the first and secondwebsites, respectively. The search results may be received in responseto a search request being input by a user of a user device that isconnected to the Bayesian matching server. In 320, the method furtherincludes calculating a probability that a search result of a firstwebsite corresponds to a same item as a search result of a secondwebsite based on Bayes theorem. For example, the calculating in 320 mayinclude calculating the probability that the search result of the firstwebsite and the search result of the second website correspond to a samerental property listing, product, or service. As one example, aprobability that a rental property listing of the first websitecorresponds to a same property as a rental property listing of thesecond website may be calculated based on Bayes theorem.

According to various embodiments, the probability may be calculatedbased on web content of the search result of the first website and webcontent of the search result of the second website being compared usingBayes theorem. For example, the probability may be calculated based onone or more attributes of the respective search results such as a name,an address, a geolocation, amenity information, ratings, and the like,of the rental property listings of the first and second websites beingcompared using Bayes theorem. Here, keywords from the names or keywordsand numbers from the addresses may be input into Bayes theorem todetermine a probability that the first and second search results are forthe same item. Also, Bayes theorem may be used to find a plurality ofprobabilities based on a plurality of different attributes and theplurality of probabilities may be averaged or otherwise combined todetermine whether the two search results are a match.

In 330, the method further includes, in response to the calculatedprobability being greater than a predetermined threshold, determiningthat the search result of the first website and the search result of thesecond website are a match. Furthermore, in 340 the method includesdisplaying an aggregated list of search results combined from the firstwebsite and the second website based on the matched search results. Forexample, the displaying may include displaying a comparison of webcontent from the search result of the first website with web contentfrom the search result of the second website. As another example, thedisplaying may include displaying the search result from the firstwebsite within the aggregated list and excluding the search result fromthe second website from the aggregated list as being redundant as thesearch result from the first website.

FIG. 4 illustrates a computing device 400 for matching search resultsfrom multiple websites in accordance with an example embodiment. Forexample, the computing device 400 may correspond to the Bayesianmatching server 120 or 220 of FIG. 1 or 2, and may perform the method300 of FIG. 3. Referring to FIG. 4, the computing device 400 includes anetwork interface 410, a processor 420, a memory 430, and an output 440.Although not shown in FIG. 4, the computing device 400 may include othercomponents, for example, an input unit, a transmitter, a receiver, andthe like. The network interface 410 may transmit and receive data over anetwork such as the Internet. For example, the network interface 410 maytransmit and receive data to and from user devices, content servers, webservers, and the like. The processor 420 may include a single coreprocessing device, a multicore processing device, or multiple processingdevices. The processor 420 may control the overall operations of thecomputing device 400. The memory 430 may include any desired memory, forexample, random access memory (RAM), one or more hard disks, cache,hybrid memory, an external memory, flash memory, and the like.

In the example of FIG. 4, the network interface 410 may receive orotherwise collect search results from a plurality of sources such assearch results from a first website and search results from a secondwebsite. As an example, the search results may be collected or receivedfrom the first and second websites in response to a user query input ona third website (e.g., hosted by the computing device 400 or some otherdevice), or input on one of the first or second websites. In someexamples, the search results may be stored in the memory 430. Theprocessor 420 may calculate a probability that a search result of afirst website corresponds to a same item as a search result of a secondwebsite based on Bayes theorem. In response to the calculatedprobability being greater than a predetermined threshold, the processor420 may further determine that the search result of the first websiteand the search result of the second website are a match. For example,the processor 420 may calculate the probability based on web content ofthe search result of the first website and web content of the searchresult of the second website being compared using Bayes theorem.

As a non-limiting example, the processor 420 may calculate theprobability that the search result of the first website and the searchresult of the second website correspond to a same rental propertylisting, product, or service. That is, the processor 420 may calculate aprobability that a rental property listing of a first websitecorresponds to a same property as a rental property listing of a secondwebsite based on Bayes theorem. In this example, the processor 420 mayuse one or more of a name, an address, a geolocation, features,amenities, ratings, and the like, of the rental properties as inputsinto a Bayes theorem algorithm to determine a likelihood that the searchresult of the first website corresponds to a same rental property as asearch result of a second website.

The output 440 may output, to a display device such as the displaydevice of a user device through the Internet, an aggregated list ofsearch results combined from the first website and the second websitebased on the matched search results. For example, the output 440 mayoutput a display of a comparison of web content from the search resultof the first website with web content from the search result of thesecond website. As another example, the output 440 may output theaggregated list on search results including the search result from thefirst website within the aggregated list and excluding the search resultfrom the second website from the aggregated list as being redundant asthe search result from the first website.

As will be appreciated based on the foregoing specification, theabove-described examples of the disclosure may be implemented usingcomputer programming or engineering techniques including computersoftware, firmware, hardware or any combination or subset thereof. Anysuch resulting program, having computer-readable code, may be embodiedor provided within one or more non transitory computer-readable media,thereby making a computer program product, i.e., an article ofmanufacture, according to the discussed examples of the disclosure. Forexample, the non-transitory computer-readable media may be, but is notlimited to, a fixed drive, diskette, optical disk, magnetic tape, flashmemory, semiconductor memory such as read-only memory (ROM), and/or anytransmitting/receiving medium such as the Internet, cloud storage, theinternet of things, or other communication network or link. The articleof manufacture containing the computer code may be made and/or used byexecuting the code directly from one medium, by copying the code fromone medium to another medium, or by transmitting the code over anetwork.

The computer programs (also referred to as programs, software, softwareapplications, “apps”, or code) may include machine instructions for aprogrammable processor, and may be implemented in a high-levelprocedural and/or object-oriented programming language, and/or inassembly/machine language. As used herein, the terms “machine-readablemedium” and “computer-readable medium” refer to any computer programproduct, apparatus, cloud storage, internet of things, and/or device(e.g., magnetic discs, optical disks, memory, programmable logic devices(PLDs)) used to provide machine instructions and/or data to aprogrammable processor, including a machine-readable medium thatreceives machine instructions as a machine-readable signal. The“machine-readable medium” and “computer-readable medium,” however, donot include transitory signals. The term “machine-readable signal”refers to any signal that may be used to provide machine instructionsand/or any other kind of data to a programmable processor.

The above descriptions and illustrations of processes herein should notbe considered to imply a fixed order for performing the process steps.Rather, the process steps may be performed in any order that ispracticable, including simultaneous performance of at least some steps.Although the disclosure has been described in connection with specificexamples, it should be understood that various changes, substitutions,and alterations apparent to those skilled in the art can be made to thedisclosed embodiments without departing from the spirit and scope of thedisclosure as set forth in the appended claims.

What is claimed is:
 1. A method for matching search results frommultiple websites, the method comprising: calculating a probability thata search result of a first website corresponds to a same item as asearch result of a second website based on Bayes theorem; in response tothe calculated probability being greater than a predetermined threshold,determining that the search result of the first website and the searchresult of the second website are a match; and displaying an aggregatedlist of search results combined from the first website and the secondwebsite based on the matched search results.
 2. The method of claim 1,wherein the calculating comprises calculating the probability that thesearch result of the first website and the search result of the secondwebsite correspond to a same rental property listing, product, orservice.
 3. The method of claim 1, wherein the probability is calculatedbased on web content of the search result of the first website and webcontent of the search result of the second website being compared usingBayes theorem.
 4. The method of claim 1, wherein the calculating theprobability comprises calculating a probability that a rental propertylisting of the first website corresponds to a same property as a rentalproperty listing of the second website based on Bayes theorem.
 5. Themethod of claim 4, wherein the probability is calculated based on a nameof the rental property listing of the first website and a name of therental property listing of the second website being compared using Bayestheorem.
 6. The method of claim 4, wherein the probability is calculatedbased on an address of the rental property listing of the first websiteand an address of the rental property listing of the second websitebeing compared using Bayes theorem.
 7. The method of claim 1, whereinthe displaying comprises displaying a comparison of web content from thesearch result of the first website with web content from the searchresult of the second website.
 8. The method of claim 1, wherein thedisplaying comprises displaying the search result from the first websitewithin the aggregated list and excluding the search result from thesecond website from the aggregated list as being redundant as the searchresult from the first website.
 9. A computing device for matching searchresults from multiple websites, the computing device comprising: aprocessor configured to calculate a probability that a search result ofa first website corresponds to a same item as a search result of asecond website based on Bayes theorem, and, in response to thecalculated probability being greater than a predetermined threshold,determine that the search result of the first website and the searchresult of the second website are a match; and an output configured tooutput, to a display device, an aggregated list of search resultscombined from the first website and the second website based on thematched search results.
 10. The computing device of claim 9, wherein theprocessor is configured to calculate the probability that the searchresult of the first website and the search result of the second websitecorrespond to a same rental property listing, product, or service. 11.The computing device of claim 9, the processor is configured tocalculate the probability based on web content of the search result ofthe first website and web content of the search result of the secondwebsite being compared using Bayes theorem.
 12. The computing device ofclaim 9, wherein the processor is configured to calculate a probabilitythat a rental property listing of the first website corresponds to asame property as a rental property listing of the second website basedon Bayes theorem.
 13. The computing device of claim 12, wherein theprocessor calculates the probability based on a name of the rentalproperty listing of the first website and a name of the rental propertylisting of the second website being compared using Bayes theorem. 14.The computing device of claim 12, wherein the processor calculates theprobability based on an address of the rental property listing of thefirst website and an address of the rental property listing of thesecond website being compared using Bayes theorem.
 15. The computingdevice of claim 9, wherein the output is configured to output a displayof a comparison of web content from the search result of the firstwebsite with web content from the search result of the second website.16. The computing device of claim 9, wherein the output is configured tooutput the aggregated list on search results including the search resultfrom the first website within the aggregated list and excluding thesearch result from the second website from the aggregated list as beingredundant as the search result from the first website.
 17. Anon-transitory computer readable medium having stored thereininstructions that when executed cause a computer to perform a method formatching search results from multiple websites, the method comprising:calculating a probability that a search result of a first websitecorresponds to a same item as a search result of a second website basedon Bayes theorem; in response to the calculated probability beinggreater than a predetermined threshold, determining that the searchresult of the first website and the search result of the second websiteare a match; and displaying an aggregated list of search resultscombined from the first website and the second website based on thematched search results.
 18. The non-transitory computer readable mediumof claim 17, wherein the probability is calculated based on web contentof the search result of the first website and web content of the searchresult of the second website being compared using Bayes theorem.
 19. Thenon-transitory computer readable medium of claim 17, wherein thecalculating the probability comprises calculating a probability that arental property listing of the first website corresponds to a sameproperty as a rental property listing of the second website based onBayes theorem.
 20. The non-transitory computer readable medium of claim17, wherein the probability is calculated based on a name of the rentalproperty listing of the first website and a name of the rental propertylisting of the second website being compared using Bayes theorem.